Conventional techniques for risk prediction during oil and gas drilling operations typically only consider a single model or a single approach to risk prediction. One disadvantage of such techniques includes losing precision in time-based prediction results due to training with large data sets. In addition, such techniques train their models by partitioning the historical data into three different time segments: i) when all drilling conditions are normal; ii) when risk realization is imminent; and iii) when the risk is actually realized such as, for example, stuck pipe. In most cases, the historical data for time segment (iii) reveals drastic changes compared to the other time segments. The historical data that comes from time segment (iii) thus, overwhelms the historical data for the other two time segments, which decreases the accuracy of predicting when risk realization is imminent in time segment (ii). Some conventional techniques also may only use a historical data from a single well for training, which may not be enough data to accurately describe the attributes of existing wells or new wells with the same geography.